"Uncovering neural substrates across Alzheimer's disease stages using c" by Yan Tang, Chao Yang et al.
 

Uncovering neural substrates across Alzheimer's disease stages using contrastive variational autoencoder

Document Type

Journal Article

Publication Date

10-3-2024

Journal

Cerebral cortex (New York, N.Y. : 1991)

Volume

34

Issue

10

DOI

10.1093/cercor/bhae393

Keywords

Alzheimer’s disease; brain morphology; contrastive variational autoencoder; neurofilament light chain; structural magnetic resonance imaging

Abstract

Alzheimer's disease is the most common major neurocognitive disorder. Although currently, no cure exists, understanding the neurobiological substrate underlying Alzheimer's disease progression will facilitate early diagnosis and treatment, slow disease progression, and improve prognosis. In this study, we aimed to understand the morphological changes underlying Alzheimer's disease progression using structural magnetic resonance imaging data from cognitively normal individuals, individuals with mild cognitive impairment, and Alzheimer's disease via a contrastive variational autoencoder model. We used contrastive variational autoencoder to generate synthetic data to boost the downstream classification performance. Due to the ability to parse out the nonclinical factors such as age and gender, contrastive variational autoencoder facilitated a purer comparison between different Alzheimer's disease stages to identify the pathological changes specific to Alzheimer's disease progression. We showed that brain morphological changes across Alzheimer's disease stages were significantly associated with individuals' neurofilament light chain concentration, a potential biomarker for Alzheimer's disease, highlighting the biological plausibility of our results.

Department

Neurology

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